Simultaneous particle image velocimetry and infrared imagery of microscale breaking waves
Bibliographic record
Abstract
We report the results from a laboratory investigation in which microscale breaking waves were detected using an infrared (IR) imager and two-dimensional (2-D) velocity fields were simultaneously measured using particle image velocimetry (PIV). In addition, the local heat transfer velocity was measured using the controlled flux technique. To the best of our knowledge these are the first measurements of the instantaneous 2-D velocity fields generated beneath microscale breaking waves. Careful measurements of the water surface profile enabled us to make accurate estimates of the near-surface velocities using PIV. Previous experiments have shown that behind the leading edge of a microscale breaker the cool skin layer is disrupted creating a thermal signature in the IR image [Jessup et al., J. Geophys. Res. 102, 23145 (1997)]. The simultaneously sampled IR images and PIV data enabled us to show that these disruptions or wakes are typically produced by a series of vortices that form behind the leading edge of the breaker. When the vortices are first formed they are very strong and coherent but as time passes, and they move from the crest region to the back face of the wave, they become weaker and less coherent. The near-surface vorticity was correlated with both the fractional area coverage of microscale breaking waves and the local heat transfer velocity. The strong correlations provide convincing evidence that the wakes produced by microscale breaking waves are regions of high near-surface vorticity that are in turn responsible for enhancing air–water heat transfer rates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".